Adaptive friction compensation using neural network approximations

被引：75

作者：

Huang, SN ^{[1
]}

Tan, KK ^{[1
]}

Lee, TH ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Elect Engn, Singapore 117548, Singapore

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2000年 / 30卷 / 04期

关键词：

adaptive systems; compensation; friction; neural networks;

D O I：

10.1109/5326.897081

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a new compensation technique for a friction model, which captures problematic friction effects such as Stribeck effects, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction. The proposed control utilizes a PD control structure and an adaptive estimate of the friction force. Specifically, a radial basis function (RBF) Is used to compensate the effects of the unknown nonlinearly occurring Stribeck parameter in the friction model. The main analytical result Is a stability theorem fur the proposed compensator which can achieve regional stability of the closed-loop system. Furthermore, we show that the transient performance of the resulting adaptive system is analytically quantified. To support the theoretical concepts, we present dynamic simulations fur the proposed control scheme.

引用

页码：551 / 557

页数：7

共 27 条

[11] Robust adaptive NN feedback linearization control of nonlinear systems [J].

Ge, SS .

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 1996, 27 (12) :1327-1338

[12] Control of nonlinear systems with friction [J].

Hirschorn, RM ;

Miller, G .

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 1999, 7 (05) :588-595

[13] MULTILAYER FEEDFORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS [J].

HORNIK, K ;

STINCHCOMBE, M ;

WHITE, H .

NEURAL NETWORKS, 1989, 2 (05) :359-366

[14]

Kiguchi K, 1996, IEEE INT CONF ROBOT, P372, DOI 10.1109/ROBOT.1996.503805

[15]

Kiguchi K., 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), P448, DOI 10.1109/IROS.1999.813045

[16] Optimal design of CMAC neural-network controller for robot manipulators [J].

Kim, YH ;

Lewis, FL .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2000, 30 (01) :22-31

[17]

Kristic M., 1995, Nonlinear and Adaptive Control Design

[18]

LEONARD NE, 1992, PROCEEDINGS OF THE 31ST IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4, P267, DOI 10.1109/CDC.1992.371741

[19]

LI W, 1994, IEEE T CONTR SYST T, V2, P265

[20] Linear motor motion control using a learning feedforward controller [J].

Otten, G ;

deVries, TJA ;

vanAmerongen, J ;

Rankers, AM ;

Gaal, EW .

IEEE-ASME TRANSACTIONS ON MECHATRONICS, 1997, 2 (03) :179-187

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